Compare xCell cell types
x <- data.frame(RNA_xcell)
x['TP_53'] <- as.factor(cl$TP_53)
x <- na.omit(x)
x <- reshape2::melt(x, id.vars = c('TP_53'))
violin_deconv <- function(x, ct) {
x_sub <- x[which(x$variable == ct),]
dp <- ggplot(x_sub, aes(x=TP_53, y=value, fill=TP_53)) +
geom_violin(trim=FALSE) +
geom_boxplot(width=0.1, fill="white") +
labs(title=paste0(ct, " - XCell deconvolution"),x="pt group", y = "Enrichment score") +
scale_fill_brewer(palette="Dark2") + theme_minimal() +
ggsignif::geom_signif(comparisons = list(c("0", "1")),
map_signif_level=TRUE)
dp
}
xcell_ct <- colnames(RNA_xcell)
for (i in xcell_ct){
plot(violin_deconv(x, ct = i))
}
## Warning in wilcox.test.default(c(0, 0.0085, 0, 0, 0, 0, 0, 0, 0, 0, 0.0062, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0396, 0, 0, 0.2611, 0.0438, 0, 0.0482, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.2829, 0.0549, 0, 0, 0, 0.0068, 0, 0.0776, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0224, 0, 0, 0.0312, 0, 0, 0.0464, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0029, 0.0153, 0.0539, 0, 0.0223, 6e-04, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0668, 0.0823, 0, 0.1576, 4e-04, 0.0798, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0118, 0.0307, 0, 0, 0.0485, 0.0106, 0.0014, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.1122, 0.004, 0, 0.1344, 0.0127, 0, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0774, 0.0299, 0, 0.1108, 0.0061, 0, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0451, 0.0147, 0, 0.0884, 0, 0, 0.0364, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0578, 0.0148, 0, 0.0357, 0, 0, 0, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0054, 3e-04, 0, 0.0034, 0, 0.0013, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0.0036, 0.0078, 0.0083, 0.0115, 0.0421, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0081, 0, 0.012, 0, 0, 0, 0, 0, 0, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0.1264, 0.1048, 0, 0.0905, 0.0655, 0.0453, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0306, 0.0219, 0.0346, 0, 0.1207, 0.0651, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0128, 0.0114, 0.004, 0, 0, 0.0012, 0, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.1926, 0.1741, 0.0551, 0.2679, 0.0486, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0043, 0.0046, 1e-04, 0, 0, 0, 0, 0, 0, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0541, 0.091, 0.2541, 0.1425, 0.1426, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0.2287, 0.3249, 0.0653, 0.2771, 0.1111, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0014, 0, 0.033, 0, 0.0085, 0, 0, 0, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.3187, 0.2078, 0.1312, 0.4575, 0.0889, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0018, 0, 0, 0.007, 0, 0, 0, 0, 0, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0019, 5e-04, 0.019, 0.0083, 0.0077, 0.0105, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0.0506, 0.0196, 0.0242, 0.0063, 0.0716, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.1354, 0.0114, 0, 0.0238, 0, 0.1263, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0081, 0.1566, 0.0244, 0.036, 0.0119, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0625, 0.017, 0.013, 0.0081, 0.0093, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0619, 0.1094, 0.0208, 0.064, 0, 0.0426, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.057, 0.052, 0.0127, 0.0441, 0.0501, 0.0444, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0066, 0.0054, 0, 0.0108, 1e-04, 0.0026, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0047, 0.0081, 0, 0.0135, 0, 0.0087, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0, 0, 0.0831, 0, 0, 0, 0, 0, 0.002, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0127, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0472, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0234, 0, 0, 0, 0, 0, 0.054, 0, 0, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0204, 0.0122, 0.0144, 0.0184, 0.0032, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.028, 0.0456, 0, 0.0263, 0, 0.007, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0026, 0.0012, 7e-04, 0.0029, 0.0039, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0024, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0.1068, 0, 0.0166, 0, 0.1044, 0.0115, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0306, 0, 0, 0.0488, 0, 0.0866, 0, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0011, 0, 0.0353, 0, 0.0725, 0.0062, 0.0458, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0041, 0.0071, 0, 0.0034, 0.007, 0, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.133, 0.0177, 0.0705, 0.0498, 0.0636, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 4e-04, 0.0123, 0, 0.0088, 0.0036, 0.0059, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.002, 0, 0, 0, 0, 0, 0, 0, 0.0217, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0.0641, 0, 0.046, 0, 0.0393, 0.0542, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.054, 0.0177, 0.0077, 0.0162, 0.0102, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 5e-04, 0, 0, 0, 0, 0, 0.0538, 0, 0, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0337, 0, 0, 0.0379, 0, 0, 0.0093, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0477, 0.076, 0.0098, 0.0498, 0.0176, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0.5017, 0.4452, 0.2353, 0.1457, 0.0375, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.1447, 0.0936, 0.0221, 0.1745, 0.014, : cannot
## compute exact p-value with ties
## Warning in wilcox.test.default(c(0.0808, 0.1142, 0.037, 0.0985, 0.027, 0.0334, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0084, 0, 0, 0.018, 0.0064, 0, 0.0081, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0.0019, 0, 0, 0, 0, 0, 0.0158, 0, 0.0059, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0, 0, 0, 0, 0.0127, 0, 0, 1e-04, 0, 0.0037, :
## cannot compute exact p-value with ties
## Warning in wilcox.test.default(c(0.2106, 0.2538, 0.0602, 0.2725, 0.0798, :
## cannot compute exact p-value with ties
DEG
DE_res_1v2 <- DE_analysis(ls_preprocessed2,
GeneBased=FALSE,
pDataBased=FALSE,
NewCondition=TRUE,
NewCondition_df = p_all_cl,
cond_nm='TP_53',
two_levels=c('0','1'),
reference = '0',
correct_gender=TRUE)
## Unlist done
## # A tibble: 52 x 5
## Vantage_ID pt_ID Batch TP_53 Gender
## <chr> <chr> <chr> <chr> <fct>
## 1 R3388_YZ_46 11424 1 1 Male
## 2 R3388_YZ_1 11601 1 0 Male
## 3 R3388_YZ_2 11646 1 0 Male
## 4 R3388_YZ_4 11652 1 0 Male
## 5 R3388_YZ_44 11817 1 1 Female
## 6 R3388_YZ_3 11820 1 0 Male
## 7 R4163_YZ_28 11840 2 1 Male
## 8 R3388_YZ_5 11855 1 0 Male
## 9 R3388_YZ_43 11938 1 0 Male
## 10 R3388_YZ_59 11957 1 0 Male
## # … with 42 more rows
## # A tibble: 52 x 5
## Vantage_ID pt_ID Batch Condition Gender
## <chr> <chr> <chr> <chr> <fct>
## 1 R3388_YZ_46 11424 1 1 Male
## 2 R3388_YZ_1 11601 1 0 Male
## 3 R3388_YZ_2 11646 1 0 Male
## 4 R3388_YZ_4 11652 1 0 Male
## 5 R3388_YZ_44 11817 1 1 Female
## 6 R3388_YZ_3 11820 1 0 Male
## 7 R4163_YZ_28 11840 2 1 Male
## 8 R3388_YZ_5 11855 1 0 Male
## 9 R3388_YZ_43 11938 1 0 Male
## 10 R3388_YZ_59 11957 1 0 Male
## # … with 42 more rows
## # A tibble: 52 x 5
## Vantage_ID pt_ID Batch Condition Gender
## <chr> <chr> <chr> <chr> <fct>
## 1 R3388_YZ_46 11424 1 1 Male
## 2 R3388_YZ_1 11601 1 0 Male
## 3 R3388_YZ_2 11646 1 0 Male
## 4 R3388_YZ_4 11652 1 0 Male
## 5 R3388_YZ_44 11817 1 1 Female
## 6 R3388_YZ_3 11820 1 0 Male
## 7 R4163_YZ_28 11840 2 1 Male
## 8 R3388_YZ_5 11855 1 0 Male
## 9 R3388_YZ_43 11938 1 0 Male
## 10 R3388_YZ_59 11957 1 0 Male
## # … with 42 more rows
## # A tibble: 52 x 5
## Vantage_ID pt_ID Batch Condition Gender
## <chr> <chr> <chr> <chr> <fct>
## 1 R3388_YZ_46 11424 1 1 Male
## 2 R3388_YZ_1 11601 1 0 Male
## 3 R3388_YZ_2 11646 1 0 Male
## 4 R3388_YZ_4 11652 1 0 Male
## 5 R3388_YZ_44 11817 1 1 Female
## 6 R3388_YZ_3 11820 1 0 Male
## 7 R4163_YZ_28 11840 2 1 Male
## 8 R3388_YZ_5 11855 1 0 Male
## 9 R3388_YZ_43 11938 1 0 Male
## 10 R3388_YZ_59 11957 1 0 Male
## # … with 42 more rows
## Labeling done
## Filtering done
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## Design done
## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors
## Warning: Setting row names on a tibble is deprecated.
## vsd symbols done
## using pre-existing size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
## -- replacing outliers and refitting for 2217 genes
## -- DESeq argument 'minReplicatesForReplace' = 7
## -- original counts are preserved in counts(dds)
## estimating dispersions
## fitting model and testing
## DESeq done
## using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).
##
## Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.
## See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.
## Reference: https://doi.org/10.1093/bioinformatics/bty895
## res symbols done
## list done
heatmap_200(DE_res_1v2$res_df, DE_res_1v2$vsd_mat_sym, DE_res_1v2$meta_data, DE_res_1v2$pData_rnaseq)
volcano_plot(DE_res_1v2$res_df, gene=NULL, p_title='TP53: WT vs Mut')
fgsea_res_1v2 <- fgsea_analysis(DE_res_1v2)
## `summarise()` ungrouping output (override with `.groups` argument)
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
## Warning in fgsea(pathways = gmtPathways(pthw_path), stats = ranks, nperm
## = 1000): You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (0.01% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
fgsea_res <- fgsea_res_1v2
cond_nm <- 'TP53: WT vs Mut'
fgsea_plot(fgsea_res$res_hm, pathways_title='Hallmark', condition_name= cond_nm)
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 HALLMARK_G2M_CHECKPOINT 0.00174 0.00542 -0.677 -3.28 0 187 down
## 2 HALLMARK_E2F_TARGETS 0.00175 0.00542 -0.650 -3.16 0 191 down
## 3 HALLMARK_EPITHELIAL_ME… 0.00176 0.00542 -0.554 -2.68 0 189 down
## 4 HALLMARK_MTORC1_SIGNAL… 0.00178 0.00542 -0.493 -2.39 0 190 down
## 5 HALLMARK_ALLOGRAFT_REJ… 0.00174 0.00542 -0.491 -2.38 0 187 down
## 6 HALLMARK_MITOTIC_SPIND… 0.00175 0.00542 -0.483 -2.35 0 195 down
## 7 HALLMARK_INFLAMMATORY_… 0.00176 0.00542 -0.455 -2.21 0 193 down
## 8 HALLMARK_INTERFERON_GA… 0.00175 0.00542 -0.443 -2.16 0 194 down
## 9 HALLMARK_IL6_JAK_STAT3… 0.00179 0.00542 -0.506 -2.16 0 82 down
## 10 HALLMARK_TNFA_SIGNALIN… 0.00175 0.00542 -0.425 -2.07 0 195 down
## # … with 20 more rows
fgsea_plot(fgsea_res$res_c1, pathways_title='C1 positional genes', condition_name= cond_nm)
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 chr19q13 0.0016 0.0103 -0.493 -2.73 0 769 down
## 2 chr16q22 0.00230 0.0103 0.572 2.66 0 132 up
## 3 chr4q35 0.00220 0.0103 0.625 2.38 0 43 up
## 4 chr5q13 0.00220 0.0103 0.566 2.37 0 70 up
## 5 MT 0.00220 0.0103 0.672 2.34 0 31 up
## 6 chr16q12 0.00224 0.0103 0.600 2.33 0 48 up
## 7 chr3q21 0.00175 0.0103 -0.523 -2.25 0 90 down
## 8 chr2q21 0.00182 0.0103 -0.563 -2.23 0 60 down
## 9 chr5q21 0.00212 0.0103 0.657 2.20 0 26 up
## 10 chr1p33 0.00223 0.0103 0.588 2.18 0 40 up
## # … with 20 more rows
fgsea_plot(fgsea_res$res_c2, pathways_title='C2 curated genes', condition_name= cond_nm)
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 SOTIRIOU_BREAST_CANCER_… 0.00180 0.0135 -0.818 -3.79 0 138 down
## 2 ROSTY_CERVICAL_CANCER_P… 0.00181 0.0135 -0.820 -3.78 0 130 down
## 3 SHEDDEN_LUNG_CANCER_POO… 0.00173 0.0135 -0.679 -3.58 0 426 down
## 4 KONG_E2F3_TARGETS 0.00187 0.0135 -0.810 -3.49 0 88 down
## 5 DUTERTRE_ESTRADIOL_RESP… 0.00180 0.0135 -0.680 -3.48 0 305 down
## 6 WHITEFORD_PEDIATRIC_CAN… 0.00189 0.0135 -0.785 -3.47 0 108 down
## 7 CROONQUIST_IL6_DEPRIVAT… 0.00184 0.0135 -0.791 -3.44 0 91 down
## 8 ZHOU_CELL_CYCLE_GENES_I… 0.00187 0.0135 -0.766 -3.44 0 116 down
## 9 FLORIO_NEOCORTEX_BASAL_… 0.00180 0.0135 -0.713 -3.43 0 171 down
## 10 KOBAYASHI_EGFR_SIGNALIN… 0.00183 0.0135 -0.680 -3.41 0 239 down
## # … with 20 more rows
fgsea_plot(fgsea_res$res_c3, pathways_title='C3 regulatory target genes', condition_name= cond_nm)
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 HSD17B8_TARGET_GENES 0.00159 0.0350 -0.572 -3.07 0 539 down
## 2 E2F_Q6 0.00177 0.0350 -0.509 -2.48 0 212 down
## 3 E2F1_Q6 0.00176 0.0350 -0.504 -2.46 0 215 down
## 4 E2F4DP1_01 0.00176 0.0350 -0.499 -2.45 0 219 down
## 5 E2F_Q4 0.00176 0.0350 -0.500 -2.44 0 215 down
## 6 SGCGSSAAA_E2F1DP2_01 0.00176 0.0350 -0.523 -2.44 0 153 down
## 7 E2F_Q4_01 0.00180 0.0350 -0.496 -2.42 0 216 down
## 8 E2F1DP1RB_01 0.00176 0.0350 -0.495 -2.41 0 213 down
## 9 E2F_02 0.00179 0.0350 -0.486 -2.37 0 218 down
## 10 E2F1DP1_01 0.00179 0.0350 -0.485 -2.37 0 218 down
## # … with 20 more rows
fgsea_plot(fgsea_res$res_c4, pathways_title='C4 cancer', condition_name= cond_nm)
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 GNF2_CCNA2 0.00198 0.00852 -0.867 -3.53 0 65 down
## 2 MODULE_54 0.00174 0.00852 -0.697 -3.48 0 241 down
## 3 GNF2_PCNA 0.00198 0.00852 -0.854 -3.47 0 65 down
## 4 GNF2_CCNB2 0.00190 0.00852 -0.886 -3.47 0 54 down
## 5 GNF2_CDC20 0.00187 0.00852 -0.877 -3.43 0 53 down
## 6 GNF2_CDC2 0.00192 0.00852 -0.852 -3.42 0 59 down
## 7 GNF2_MCM4 0.00187 0.00852 -0.872 -3.40 0 52 down
## 8 GNF2_BUB1B 0.00189 0.00852 -0.870 -3.36 0 49 down
## 9 GNF2_CENPF 0.00189 0.00852 -0.836 -3.35 0 58 down
## 10 GNF2_HMMR 0.00195 0.00852 -0.881 -3.33 0 46 down
## # … with 20 more rows
fgsea_plot(fgsea_res$res_c5, pathways_title='C5 GO genes', condition_name= cond_nm)
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 GO_SISTER_CHROMATID_SEG… 0.00175 0.0304 -0.588 -2.78 0 171 down
## 2 GO_MITOTIC_SISTER_CHROM… 0.00180 0.0304 -0.593 -2.72 0 142 down
## 3 GO_DNA_DEPENDENT_DNA_RE… 0.00178 0.0304 -0.586 -2.69 0 138 down
## 4 GO_NUCLEAR_CHROMOSOME_S… 0.00174 0.0304 -0.548 -2.68 0 230 down
## 5 GO_CHROMOSOME_CENTROMER… 0.00175 0.0304 -0.563 -2.66 0 172 down
## 6 GO_REGULATION_OF_CHROMO… 0.00185 0.0304 -0.624 -2.66 0 92 down
## 7 GO_CHROMOSOME_SEGREGATI… 0.00169 0.0304 -0.527 -2.66 0 287 down
## 8 GO_COLLAGEN_FIBRIL_ORGA… 0.00189 0.0304 -0.695 -2.64 0 51 down
## 9 GO_CELL_CYCLE_DNA_REPLI… 0.00190 0.0304 -0.668 -2.64 0 61 down
## 10 GO_CHROMOSOMAL_REGION 0.00169 0.0304 -0.514 -2.61 0 298 down
## # … with 20 more rows
fgsea_plot(fgsea_res$res_c6, pathways_title='C6 oncogenic', condition_name= cond_nm)
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 RB_P107_DN.V1_UP 0.00180 0.0130 -0.597 -2.75 0 128 down
## 2 CSR_LATE_UP.V1_UP 0.00181 0.0130 -0.531 -2.50 0 156 down
## 3 PRC2_EED_UP.V1_DN 0.00176 0.0130 -0.483 -2.30 0 179 down
## 4 BMI1_DN_MEL18_DN.V1_UP 0.00181 0.0130 -0.497 -2.29 0 135 down
## 5 PRC2_EZH2_UP.V1_DN 0.00179 0.0130 -0.481 -2.28 0 173 down
## 6 RPS14_DN.V1_DN 0.00180 0.0130 -0.480 -2.28 0 172 down
## 7 LEF1_UP.V1_UP 0.00176 0.0130 -0.453 -2.16 0 179 down
## 8 SNF5_DN.V1_UP 0.00182 0.0130 -0.459 -2.15 0 152 down
## 9 ATF2_UP.V1_UP 0.00181 0.0130 -0.446 -2.10 0 165 down
## 10 E2F1_UP.V1_UP 0.00179 0.0130 -0.440 -2.09 0 173 down
## # … with 20 more rows
fgsea_plot(fgsea_res$res_c7, pathways_title='C7 immunologic', condition_name= cond_nm)
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 GSE13547_CTRL_VS_ANTI_… 0.00176 0.00855 -0.721 -3.44 0 174 down
## 2 GSE15750_DAY6_VS_DAY10… 0.00177 0.00855 -0.712 -3.41 0 181 down
## 3 GSE14415_NATURAL_TREG_… 0.00176 0.00855 -0.689 -3.28 0 173 down
## 4 GSE15750_DAY6_VS_DAY10… 0.00177 0.00855 -0.676 -3.26 0 188 down
## 5 GSE39556_CD8A_DC_VS_NK… 0.00176 0.00855 -0.667 -3.22 0 185 down
## 6 GSE24634_TEFF_VS_TCONV… 0.00176 0.00855 -0.660 -3.19 0 192 down
## 7 GSE25088_WT_VS_STAT6_K… 0.00178 0.00855 -0.664 -3.17 0 176 down
## 8 GSE30962_PRIMARY_VS_SE… 0.00177 0.00855 -0.653 -3.14 0 184 down
## 9 GSE36476_CTRL_VS_TSST_… 0.00178 0.00855 -0.655 -3.14 0 183 down
## 10 GSE12845_IGD_POS_BLOOD… 0.00176 0.00855 -0.646 -3.12 0 186 down
## # … with 20 more rows
fgsea_plot(fgsea_res$res_msg, pathways_title='All signatures', condition_name= cond_nm)
## # A tibble: 30 x 8
## pathway pval padj ES NES nMoreExtreme size state
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <chr>
## 1 SOTIRIOU_BREAST_CANCER_… 0.00187 0.0152 -0.818 -3.78 0 138 down
## 2 ROSTY_CERVICAL_CANCER_P… 0.00185 0.0152 -0.820 -3.75 0 130 down
## 3 SHEDDEN_LUNG_CANCER_POO… 0.00172 0.0152 -0.679 -3.60 0 426 down
## 4 WHITEFORD_PEDIATRIC_CAN… 0.00181 0.0152 -0.785 -3.50 0 108 down
## 5 GNF2_CCNA2 0.00188 0.0152 -0.867 -3.50 0 65 down
## 6 DUTERTRE_ESTRADIOL_RESP… 0.00182 0.0152 -0.680 -3.49 0 305 down
## 7 KONG_E2F3_TARGETS 0.00180 0.0152 -0.810 -3.49 0 88 down
## 8 GNF2_CCNB2 0.00187 0.0152 -0.886 -3.48 0 54 down
## 9 MODULE_54 0.00186 0.0152 -0.697 -3.48 0 241 down
## 10 GSE13547_CTRL_VS_ANTI_I… 0.00180 0.0152 -0.721 -3.45 0 174 down
## # … with 20 more rows
